[action-translation] resync: likelihood_ratio_process.md (#123)

* 🔄 resync likelihood_ratio_process.md

* Add missing 'import matplotlib as mpl' for the i18n font config

The resync kept the CJK font-configuration block but dropped the mpl
import it depends on - a NameError at execution that non-strict builds
swallow. Found sweeping the wave for the pattern behind zh-cn#97's
December fixes.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Restore trailing newline (engine issue tracked in QuantEcon/action-translation#116)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Add missing bibtex key Blume_Easley2006 to fix dangling citation

The resync of likelihood_ratio_process.md pulled in a {cite}`Blume_Easley2006`
reference from upstream, but the shared quant-econ.bib was not synced, so the
strict build (-W) failed on "could not find bibtex key". Entry copied verbatim
from QuantEcon/lecture-python.myst.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Align quant-econ.bib to canonical union to avoid EOF merge conflicts

All four bibtex keys the wave needs (Lucas_Prescott_1971, Blume_Easley2006,
MaCurdy1982, Meghir2004) are now present identically across the affected
branches so the resync PRs merge without append-at-EOF conflicts. Entries
copied verbatim from QuantEcon/lecture-python.myst.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

Author: mmcky

.translate/state/likelihood_ratio_process.md.yml

@@ -0,0 +1,6 @@
+source-sha: a8966965b2649d470ea144150ca5fd5d520b5345
+synced-at: "2026-07-18"
+model: claude-sonnet-5
+mode: RESYNC
+section-count: 10
+tool-version: 0.17.0

lectures/likelihood_ratio_process.md

@@ -9,6 +9,26 @@ kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
   name: python3
+translation:
+  title: 似然比过程
+  headings:
+    Overview: 概述
+    Likelihood Ratio Process: 似然比过程
+    Nature permanently draws from density g: 当自然永久从密度g中抽取时
+    Peculiar property: 特殊性质
+    Nature permanently draws from density f: 自然永久从密度f中抽样
+    Likelihood ratio test: 似然比检验
+    Likelihood ratio test::A third distribution $h$: 第三个分布 $h$
+    Likelihood ratio test::A helpful formula: 一个有用的公式
+    Hypothesis testing and classification: 假设检验和分类
+    Hypothesis testing and classification::Model selection mistake probability: 模型选择错误概率
+    Hypothesis testing and classification::Classification: 分类
+    Hypothesis testing and classification::Error probability and divergence measures: 误差概率和散度度量
+    Markov chains: 马尔可夫链
+    Markov chains::KL divergence rate: KL散度率
+    Markov chains::Simulations: 模拟
+    Related lectures: 相关讲座
+    Exercises: 练习
 ---
 
 (likelihood_ratio_process)=
@@ -53,6 +73,7 @@ kernelspec:
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
 FONTPATH = "fonts/SourceHanSerifSC-SemiBold.otf"
+import matplotlib as mpl
 mpl.font_manager.fontManager.addfont(FONTPATH)
 plt.rcParams['font.family'] = ['Source Han Serif SC']
 
@@ -1223,7 +1244,7 @@ plt.show()
 
 显然，$e^{-C(f,g)T}$是误差率的上界。
 
-在`{doc}`divergence_measures`中，我们还研究了**Jensen-Shannon散度**作为分布之间的对称距离度量。
+在{doc}`divergence_measures`中，我们还研究了**Jensen-Shannon散度**作为分布之间的对称距离度量。
 
 我们可以使用Jensen-Shannon散度来测量分布$f$和$g$之间的距离，并计算它与模型选择错误概率的协方差。
 
@@ -1622,7 +1643,7 @@ markov_results = analyze_markov_chains(P_f, P_g)
 
 似然过程在贝叶斯学习中扮演重要角色，正如在{doc}`likelihood_bayes`中所描述的，并在{doc}`odu`中得到应用。
 
-似然比过程是Lawrence Blume和David Easley回答他们提出的问题"如果你那么聪明，为什么不富有？" {cite}`blume2006if`的核心，这是讲座{doc}`likelihood_ratio_process_2`的主题。
+似然比过程是Lawrence Blume和David Easley回答他们提出的问题"如果你那么聪明，为什么不富有？" {cite}`Blume_Easley2006`的核心，这是讲座{doc}`likelihood_ratio_process_2`的主题。
 
 似然比过程也出现在{doc}`advanced:additive_functionals`中，其中包含了另一个关于上述似然比过程**特殊性质**的说明。
 
@@ -1755,4 +1776,3 @@ $$
 
 ```{solution-end}
 ```
-